Source-linked AI summary
PCaPaint: Prostate Cancer Inpainting by Mitigating Shortcut Learning
Levente Lippenszky, Hongxu Yang, Marcell Dömötör, Krisztian Koos, László Ruskó
TL;DR
Scarce labeled data and the high-resolution, multi-sequence nature of prostate MRI make tumor-specific synthesis challenging, while latent-diffusion inpainting can reproduce the masked healthy condition. PCaPaint uses noise-filled conditioning, lesion-focused training, and separate sequence autoencoders; its synthetic data improves downstream task performance and image quality relative to DiffTumor.
Problem
Tumor-specific AI is constrained by scarce labeled data, and prostate bpMRI inpainting involves high-resolution data with sequences that have different frequency characteristics.
Source: §1 · pp. 1–2Method
PCaPaint uses Gaussian-noise lesion conditioning, a lesion-focused training objective, and separate autoencoders for T2w and DWI&ADC scans.
Source: §1 · p. 2, Latent Diffusion Model · p. 5Results
PCaPaint’s synthetic data improves segmentation, patient-level classification, and lesion-level detection, and the method outperforms DiffTumor in downstream performance and synthetic image quality.
Source: §1 · p. 2, §3.2 · p. 7, §3.3 · p. 8Takeaways & Limitations
The authors conclude that improved synthetic data for prostate lesion tasks can support development of more robust AI systems for prostate cancer care.
Source: §4 · p. 8The conditioning analysis assumes a manually annotated lesion mask that is deterministic given the lesion image.
Source: §2.1 · p. 4
Abstract
from arXiv · showhide
The development of AI systems for tumor-specific applications is limited by the scarcity of labeled data. Synthetic tumor inpainting offers a promising approach but faces challenges for prostate cancer MRI which contains high-resolution multi-sequence data. Although methods leveraging latent diffusion models (LDMs) enable large-volume synthesis, they are prone to shortcut learning, simply reproducing the condition image created by masking the lesion region. In this work, we introduce PCaPaint, a prostate cancer inpainting method based on LDMs that explicitly addresses this failure mode. To overcome shortcut learning that compromises synthetic tumor texture, we propose a simple yet efficient conditioning strategy in which the condition image is filled with Gaussian noise, and we provide theoretical justification. In addition, we propose a novel training objective for LDM that emphasizes the error within the lesion region. Furthermore, we introduce a multi-sequence latent design, in which T2w scans and DWI&ADC scans are compressed using two separate autoencoders to preserve their distinct frequency characteristics. Extensive experiments demonstrate that the generated synthetic data improves downstream performance in prostate lesion segmentation, patient-level classification and lesion-level detection. Furthermore, our method significantly outperforms a recent state-of-the-art LDM-based tumor inpainting method both in downstream performance and in synthetic image quality.
1 Introduction
Scarce and costly tumor labels constrain tumor-specific AI, while prostate MRI inpainting must handle high-resolution, multi-sequence data. PCaPaint addresses shortcut learning in latent-diffusion inpainting and is reported to improve downstream performance and synthetic image quality.
1 Introduction
- Tumor-specific AI faces scarce labeled cases, while robust deep learning also requires diverse data that is expensive and difficult to annotate.Source: §1 · p. 1
- Prostate bpMRI inpainting must address high-resolution 3D data across three sequences with distinct frequency characteristics.Source: §1 · p. 2
- With constant lesion fills, latent-diffusion inpainting can learn to reproduce the healthy condition image instead of synthesizing lesion texture.The described shortcut occurs with zero and −1 fills.Source: §1 · p. 2
- PCaPaint improves downstream performance and synthetic image quality over a state-of-the-art latent-diffusion inpainting method.The reported downstream tasks are segmentation, patient-level classification, and lesion-level detection.Source: §1 · p. 2
- PCaPaint combines Gaussian-noise conditioning, a lesion-region-weighted training objective, and separate autoencoders for T2w and DWI&ADC scans.Source: §1 · p. 2
2 Methods
PCaPaint uses Gaussian noise in the masked condition image to address shortcut learning, with theoretical justification and a lesion-focused training objective. Its pipeline combines separate latent representations for T2w and DWI&ADC scans with medically informed lesion-mask sampling.
2.1 Conditioning with Noise Fill
- Noise fill increases the training loss for the shortcut solution, encouraging the diffusion model to learn the intended denoising instead.The proposition compares constant fill with Gaussian noise fill under the assumption that the network predicts the shortcut residual.Source: §2.1 · p. 3
- When the lesion mask approaches empty, the shortcut residual converges to the true noise, allowing shortcut behavior to dominate.The theoretical analysis assumes manual lesion annotations and treats the mask as deterministic given each image.Source: §2.1 · pp. 3–4
2.2 PCaPaint
- Separate VQGAN autoencoders compress T2w scans and concatenated DWI&ADC scans to reflect their distinct frequency characteristics.T2w contains rich high-frequency anatomical detail, while ADC and DWI exhibit lower-frequency intensity patterns.Source: Autoencoders · p. 4
- PCaPaint constructs the healthy condition by replacing the lesion region with scaled, tanh-mapped Gaussian noise, then encodes it as the healthy latent.The denoising U-Net receives the healthy latent and downsampled lesion mask as conditions.Source: Latent Diffusion Model · p. 5
- A lesion-weighted objective adds a masked-region error term to the diffusion loss, with λ controlling that term’s contribution.The objective amplifies error inside the lesion region to further mitigate shortcut learning.Source: Latent Diffusion Model · p. 5
- Lesion masks are sampled using prostate-zone probabilities, gland-relative volume bounds, randomized ellipsoids, boundary perturbations, and gland clipping.The method samples lesion centers from peripheral and transition zones with probabilities of 0.75 and 0.25, respectively.Source: Sampling · p. 6
3 Experimental Results
Experiments evaluate PCaPaint’s image quality and downstream utility on PI-CAI bpMRI data. PCaPaint produces higher-quality synthetic images than DiffTumor and performs best across the three downstream tasks.
3.1 Experimental Setup
- The experiments use 900 training and 599 test bpMRI cases from PI-CAI, with 252 and 172 csPCa cases, respectively.For downstream evaluation, nnU-Net models were trained with synthetic lesions at a ratio of three synthetic lesions per real lesion.Source: Data · p. 6, §3.3 · p. 7
3.2 Image Quality Evaluation
- PCaPaint produces significantly higher image quality than DiffTumor across MRI sequences and image regions, with the best foreground and background metrics.DiffTumor with noise fill outperforms DiffTumor on foreground results; PCaPaint performs best there and in background metrics.Source: §3.2 · p. 7, Table 1 · p. 7
3.3 Prostate Lesion Downstream Tasks
- PCaPaint’s synthetic lesions show modality-specific intensity patterns: darker in T2w, brighter in DWI, and darker in ADC.The comparison uses non-lesion bpMRI cases inpainted by DiffTumor and PCaPaint with generated lesion masks.Source: §3.3 · p. 7, Figure 3 · p. 8
- PCaPaint achieves the best performance across csPCa segmentation, patient-level classification, and lesion-level detection, significantly outperforming DiffTumor.Although the Dice improvement is small, paired differences favored PCaPaint in 87 cases, versus 60 negatives and 25 ties across 172 test lesion cases.Source: §3.3 · p. 8, Table 2 · p. 8
4 Conclusion
The conclusion presents PCaPaint as an LDM-based prostate cancer inpainting method that addresses shortcut learning. Its synthetic data improves prostate lesion downstream performance, while benefits for other modalities and tumor types remain a hypothesis for future work.
4 Conclusion
- The authors hypothesize that the noise-conditioning strategy may benefit other imaging modalities and tumor types, and identify these directions for future work.The conclusion states that Proposition 1 applies broadly to diffusion-based tumor inpainting.Source: §4 · p. 8